Radiomic analysis typically involves extracting a series of quantitative features from a target tissue region of interest (ROI) via radiographic imaging. The target ROI is then described via statistics of the radiomic feature distribution (e.g. mean, skewness, kurtosis), which are then input to a machine learning classifier to make a class label prediction. Radiomics has been employed for prediction of disease aggressiveness and subtype in vivo, as well as characterizing molecular heterogeneity of tumors. However, existing approaches to radiomics-based prediction that employ statistical descriptors may not adequately capture the diversity of radiomic expression present in the target ROI, thus incompletely characterizing the underlying tissue heterogeneity.
Tumor environment heterogeneity on radiographic imaging arises due to the organization of multiple tissue pathologies or sub-compartments. For example, in Glioblastoma multiforme (GBM), the tumor region includes varied tissue types such as edema, necrotic core, and enhancing tumor. Similarly, in rectal cancer (RCa) patients that undergo neoadjuvant chemoradiation therapy, treatment effects such as fibrosis and ulceration are present both within and proximal to the tumor region. As a result of such significant tissue heterogeneity, the resulting radiomic response within and around these tumors appears highly varied, as illustrated in FIG. 1 at, which illustrate representative radiomic heatmaps 110 and 112 in RCa. Thus, existing approaches which utilize conventional statistics such as the mean or skewness value of these feature distributions may not adequately describe the diverse radiomic expression map exhibited by different disease subtypes. Existing approaches may therefore be sub-optimal in predicting outcomes or characterizing response to treatment. Consequently, there is a clinical unmet need for a more comprehensive descriptor of the organization of radiomic expression for disease characterization via radiographic imaging.